Method and apparatus for inspecting patterns formed on a substrate

ABSTRACT

The pattern inspection apparatus of the present invention performs comparison between images of regions corresponding to patterns formed to be same patterns, thereby determining mismatch portions across the images to be defects. The apparatus includes multiple sensors that synchronously acquire images of shiftable multiple detection systems different from one another, and an image comparator section corresponding thereto. In addition, the apparatus includes a means for detecting a statistical offset value from the feature amount to be a defect, thereby properly detecting the defect even when a brightness difference is occurring in association with film a thickness difference in a wafer.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of application Ser. No. 12/960,578,filed Dec. 6, 2010, which is a continuation application of applicationSer. No. 11/328,231, filed Jan. 10, 2006, which is now U.S. Pat. No.7,848,563 issued Dec. 7, 2010, the disclosure of which is herebyincorporated by reference.

BACKGROUND OF THE INVENTION

The present invention relates to pattern inspection for detection of,for example, a defect and foreign matter by using an image of an objectobtained in the manner that the object is irradiated with light or laserand is thereby imaged. More specifically, the invention relates to anapparatus and method for inspecting patterns (or, a “pattern inspectionapparatus” and a “pattern inspection method”, hereafter) well suited forexterior inspection of, for example, semiconductor wafers, thin-filmtransistors (or, “TFTs,” hereafter), and photomasks.

As an existing technology, a method of detecting a defect throughcomparison between an inspection targets image and a reference image isknown, as is disclosed in, for example, in Japanese Unexamined PatentApplication Publication No. 05-264467 (or, 1996-264467).

According to this disclosed method, inspection target samples withrepetitious patterns regularly arranged are serially imaged by a linesensor, respective imaged images are compared with an image with a timelag corresponding to a repetitious pattern pitch, and a mismatch portionis detected to be a pattern defect. An existing inspection method ofthis type will be described hereinbelow with reference to an exemplifiedcase of exterior inspection of a semiconductor wafer. As shown in FIGS.2A and 2B, a large number of chips of same patterns are arranged on aninspection target semiconductor wafer. As shown in FIG. 2B, a respectiveone of the chips can be broadly categorized into a memory mat portion601 and a peripheral circuit portion 602. The memory mat portion 601 isa group of small repetitious patterns (cells), and the peripheralcircuit portion 602, basically, is a group of random patterns.Generally, a portion such as the memory mat portion 601 has high patterndensity, and an image taken thereof by a brightfield illuminationoptical system is dark. In contrast, the peripheral circuit portion 602has low pattern density, and an image taken thereof is bright.

In the existing exterior inspection, the peripheral circuit portion 602performs an inspection by comparing same positions of adjacent chips,such as regions 61 and 62 of FIG. 2A, and detects a portion with abrightness difference greater than a threshold value to be a defect.Such inspection will be alternatively referred to as “chip comparison.”The memory mat portion 601 performs an inspection by comparing images ofadjacent cells, and similarly detects a portion with a brightnessdifference greater than a threshold value to be a defect. Suchinspection herebelow will be alternatively referred to as “cellcomparison.”

In an inspection target semiconductor wafer, planarization by CMP(chemical mechanical polishing) or the like causes delicate differencesin pattern film thickness, thereby causing local brightness differencesacross images of chips in association therewith. As in the existingmethod, when a portion where the differential value is greater than orequal to a threshold value TH is determined to be a defect, even such aregion having such brightness differences is determined to be defect.However, such a defect should not be detected as an essential defect.Such a detection result is caused in association with misreporting.Conventionally, as a method for avoiding occurrence of such amisreported event, a threshold value for defect detection is increased.However, the method causes reduction in the sensitivity, so that adefect corresponding to a differential value of an equivalent level orlower cannot be detected. Further, in the aligned chips shown in FIG.2A, a brightness difference in association with the film thicknessdifference can occur, for example, only in an area between specificchips in the wafer or in an area of a specific patter in the wafer. Whenthe threshold value TH is set in accordance with such the local area,the overall inspection sensitivity is significantly reduced or impaired.

Causes of impairing the sensitivity include variations in pattern edgethickness. FIGS. 10A to 10C are schematic view of a semiconductorpattern used as an inspection target. Gates are provided at about a 200nm pitch, and there is a linewidth variation of about 20 nm. Numeral 51in FIG. 8C represents a brightness waveform in the direction ofcomparisons of target patterns across which small linewidth variationsare occurring. In this case, there exist brightness value variations.According to the existing method of comparison inspection, when abrightness value variation such as described above is present acrossimages of adjacent cells or adjacent chips, the variation appears asnoise during the inspection.

Alternative cases are that a defect of the above-described type can bedetected through combination of factors dependant on, for example, thematerial, surface roughness, size, depth, and the like and factors, suchas illumination conditions, dependant on the detection system.

SUMMARY OF THE INVENTION

The present invention is intended to solve such problems as describedabove with the existing inspection technology. Accordingly, theinvention is intended to implement high sensitive pattern inspection inthe manner that a brightness variation across a comparison image causedin association with a film thickness difference and a pattern edgethickness difference by using a pattern inspection apparatus thatperforms comparisons of images of regions corresponding to patternsformed to be same patterns and that determines a mismatch portion to bea defect.

Further, the present invention is intended to implement preciseinspection only of a specific pattern without being influenced by aneighboring brightness variation (occurring in a neighboring portion) inthe event that the specific pattern is preliminarily known to be proneto a critical defect.

Further, the present invention performs comparison inspections undermultiple shiftable inspection conditions and then performs a comparisoninspection through either integration of the inspection results orintegration of images under different detection conditions, therebyimplementing high sensitive pattern inspection capable of being appliedto inspection for an increased variety of defects.

According to the present invention, a pattern inspection apparatus has aconfiguration that performs comparisons of images of regionscorresponding to patterns formed to be same patterns and determines amismatch portion to be a defect. In the configuration, high sensitivepattern inspection can be performed by reducing brightness variationsacross comparison images occurring in association with film thicknessdifferences and pattern edge thickness differences, thereby enablinghigh sensitive pattern inspection capable of being applied to inspectionfor a large variety of defects.

Further, according to the present invention, in the pattern inspectionapparatus, comparisons are performed under multiple shiftable inspectionconditions, and either the results thereof are integrated or thecomparison inspection is performed through integration of images underdifferent detection conditions, thereby enabling high sensitive patterninspection capable of being applied to inspection for a large variety ofdefects.

Further, according to the present invention, in the pattern inspectionapparatus, which performs comparisons of images of regions correspondingto patterns formed to be same patterns to thereby determine a mismatchportion of an image to be a defect has a configuration that includesmultiple shiftable detection systems, multiple sensors capable ofsynchronously acquiring images of multiple detection systems differentfrom one another, an image comparison method corresponding to thesensors, and a defect classification method corresponding to thesensors. Thereby, an optimal condition can be selected, and a variety ofdefects can be detected.

The configuration further includes means that creates a referencepattern by integrating information of multiple same patterns and thatperforms comparisons between the reference pattern with inspectiontarget patterns. Thereby, a defect can be detected with high sensitivityeven when a brightness difference is occurring across inter-image samepatterns in association with a pattern linewidth difference.

The configuration further includes means that extracts feature amountsfrom respective ones of multiple same patterns and that performscomparisons between the feature amounts and feature amounts extractedfrom the inspection target patterns. Thereby, even when an inter-imagebrightness difference is occurring across the same patterns inassociation with a pattern linewidth difference or the like, the defectcan be detected with high sensitivity.

The configuration further includes means that extracts defect candidatesthrough respective comparisons between the multiple same patterns andthe defect candidates and that integrates the defect candidatesextracted through the comparisons, thereby enabling sensitive defectdetection even in the case that an inter-image brightness difference isoccurring across the same patterns in association with a patternlinewidth difference.

The configuration further includes means of detecting a statisticaloffset value to be a defect candidate from the feature amount of theimage. Thereby, for the inspection targets in the wafer, even when aninter-image brightness difference is occurring across the same patternsin association with an intra-wafer film thickness difference in thewafer, a defect can be properly detected.

The configuration further includes means of collecting and integratingstatistical offset values, which are detected in a local region, from awider region; and means of integrating the offset values with pastdifferent comparison inspections and detecting a final offset value tobe a defect. Thereby, even when a large brightness difference is causedacross specific inter-image patterns in association with an intra-waferfilm thickness difference, such a difference can be restrained.

Further, the configuration includes means that retrieves a specificpattern from images of inspection target patterns and multiplecomparison patterns and that inspects only the specific patternretrieved and extracted. Thereby, even when a large brightnessdifference is caused across specific inter-image patterns in associationwith an intra-wafer film thickness difference, a critical defect can bedetected with high sensitivity.

Further, according to the present invention, pattern images of a largenumber of chips formed on the wafer are separated into images in a chipcomparison region and images in a cell comparison region, and imageprocesses of a chip comparison and a cell comparison are parallelexecuted. Thereby, image detections with the stage movement can becompleted in one time. Further, the throughput can be improved bycombination with the parallel processing of the chip comparison and thecell comparison.

Further, according to the present invention, the high sensitivityinspection can be implemented and the inspection of a large variety ofdefects can be performed by either the comparison through theintegration of images corresponding to multiple optical conditions orthe integration of comparison processes using discrete images.

Further, either brightness differences occurring across chips inassociation with various factors such as edge thickness differences orbrightness differences occurring across comparison images in associationwith inter-cell brightness differences (color variations) can berestrained by performing the comparison through integration of themultiple corresponding patterns.

Further, even higher sensitivity inspection can be implemented bydetecting the statistical offset value to be a defect.

Further, only a specific pattern prone to a critical defect can beinspected with high sensitivity.

These and other objects, features, and other advantages of the inventionwill be apparent from the following more particular description ofpreferred embodiments of the invention, as illustrated in theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings,

FIG. 1 is a block diagram an overall configuration of an opticalexterior inspection device;

FIGS. 2A and 2B, respectively, are a plan view of a semiconductor waferwith an enlarged view of chips and a plan view of the structure of oneof the chips;

FIG. 3 is a detailed block diagram of a detecting section of the opticalexterior inspection device;

FIG. 4 shows front views of screens each showing the results ofinspection and classification performed under multiple opticalconditions;

FIG. 5A is a diagram of a defect signal of an image acquired under anoptical condition A;

FIG. 5B is a diagram of a signal indicative of a difference in the eventof comparison of an image signal acquired under the optical condition Ato an adjacent cell;

FIG. 5C is a diagram of a defect signal of an image acquired under anoptical condition B;

FIG. 5D is a diagram of a signal indicative of a difference in the eventof comparison of an image signal acquired under the optical condition Bto an adjacent cell;

FIG. 5E is a diagrams of a signal indicative of a difference in theevent that images respectively acquired under the optical conditions Aand B are synthesized and then compared to an adjacent cell after;

FIG. 6 is a diagram of a process flow for detecting a defect;

FIG. 7 is a front view showing an overall configuration of a detectingsection capable of synchronously acquiring images under two opticalconditions;

FIG. 8 is a block diagram of a flow for synthesizing images acquiredunder multiple optical conditions and then performing a defect detectionprocess thereof;

FIG. 9 is a block diagram of a processing flow for synthesizing imagesacquired under multiple optical conditions and thereby performinginspection thereof;

FIG. 10A is a plan view of semiconductor patterns, showing an example ofbrightness variations in association with width differences acrossinspection target patterns;

FIG. 10B is a partly enlarged plan view of the semiconductor patterns ofFIG. 10A;

FIG. 10C is a diagram of a brightness waveform in the direction ofcomparisons of the target patterns;

FIG. 11A is a diagram of a signal waveform including a defect in anexample of cell comparison for multiple cells;

FIG. 11B is a diagram of signal waveforms of a defective image and areference image in an example of cell comparison for multiple cells;

FIG. 11C is a differential image shown in brightness when compared to anadjacent cell in an example of cell comparison for multiple cells;

FIG. 11D is a differential image with respect to a reference cell in anexample of cell comparison for multiple cells;

FIG. 12 is a diagram showing an example case of collecting brightnessvalues of six neighboring pixels;

FIG. 13A is a diagram showing arrangement and alignment in a defectcandidate extraction flow in an image comparison processing section;

FIG. 13B is a diagram of the defect candidate extraction flow in theimage comparison processing section;

FIG. 14A is a diagram showing an example of high brightness variationsacross reference images;

FIG. 14B is a diagram showing examples of high brightness variations anda defect across detection images;

FIG. 14C is a diagram showing an example of high brightness variationsacross reference images;

FIG. 14D is an example of high brightness variations across detectionimages including a defect;

FIG. 15 is a diagram showing a flow of defect detection to be performedthrough offset value integration;

FIG. 16A is a diagram showing inspection data of multiple wafers;

FIG. 16B shows a diagram illustrative of examples of offset valueintegration and multiple wafer information integration, and a plan viewof a wafer illustrative of one line of defect candidates of chips;

FIG. 17A is a view showing some of detection images of a inspectiontarget chip and reference images in left and right correspondingportions;

FIG. 17B is a view showing an example of setting specified patterns tobe templates;

FIG. 17C is a diagram of a flow of high sensitivity inspection of apattern when the pattern is preliminarily known as a pattern desired tobe precisely inspected;

FIG. 18 is a flow diagram showing an example of an elimination procedurefor a pattern prone to a nuisance defect;

FIG. 19A is a flow diagram of a process in the event that a patternsimilar to a set template is searched with high accuracy and high speed;

FIG. 19B is a view showing a pattern retrieval range;

FIG. 19C is a wafer plan view showing a chip arrangement of aninspection target wafer; and

FIG. 20 is an example case of performing graphical display of, forexample, an interim result and a detection result of inspection throughstatistical offset value detection on a GUI (graphical user interface).

DESCRIPTION OF THE PREFERRED EMBODIMENTS

One embodiment of the present invention will be described below withreference to the drawings.

In addition, a pattern inspection apparatus according to the presentembodiment includes imaging means that images optical images ofpatterns; storage means that stores multiple types of detectionconditions of the optical images for being used by the imaging means toimage the optical images of the patterns; and defect candidateextraction and classification means that sequentially detects theoptical images of the patterns through the imaging means to therebyacquire multiple images different from one another in the detectioncondition by using the multiple types of detection conditions stored inthe storage means and that extracts and classifies defect candidates byprocessing the multiple images different from one another in thedetection condition.

In addition, a pattern inspection apparatus according to the presentembodiment includes imaging means that images optical images ofpatterns; storage means that stores images acquired in a manner thatinspection target patterns and multiple comparison patterns formed tohave essentially the same shapes as the inspection target patterns aresequentially imaged by the imaging means; pattern extracting means thatsearches for and extracts a specific pattern from images of the multiplecomparison patterns; defect candidate extracting means that detectsdefect candidates from the images of the inspection target patterns byusing information of the specific pattern extracted by the patternextracting means; and defect candidate classifying means that classifiesthe defect candidates detected by the defect candidate extracting means.

The embodiment will be described with reference to an example of adefect inspection method used for an optical exterior inspection devicefor semiconductor wafers as inspection targets. FIG. 1 shows an exampleof the configuration of the optical exterior inspection device. Numeral11 denotes a sample (inspection target, such as a semiconductor wafer),numeral 12 denotes a stage movable and turnable within an XY plane andmovable in the Z direction with the sample 11 being mounted thereon, andnumeral 13 denotes a detecting section. The detection section 13 has aconfiguration including a light source 101 that irradiates the sample 11with light; an illumination optical system 102 including a lens system1021 that collects the light emitted from the light source 101, and abeamsplitter 1022 that converts a light path; an objective lens 103 thatilluminates the sample 11 with illumination light collected by theillumination optical system 102 and that images an optical imageacquired through reflection by the sample 11; an image sensor 104 thatreceives the imaged optical image and that converts the optical imageinto an image signal; and an A/D (analog/digital) converter 105 thatconverts an input signal from the image sensor 104 into a digitalsignal.

In the example shown in FIG. 1, a lamp is used as the light source 101,however, a laser may instead be used. In addition, the wavelength of thelight emitted from the light source 101 may be either a short wavelengthor broadband wavelength light (white light). For using the shortwavelength, ultraviolet wavelength light (ultraviolet light (UV light))as well may be used to enhance the resolution of an image that is to bedetected (“detection image,” hereafter) (to detect a small defect). Inthe case that, as the light source, laser, more specifically, laserhaving a single wavelength is used, means (not shown) for reducing thecoherence should be provided either in the interior of the illuminationoptical system 102 or between the light source 101 and the illuminationoptical system 102.

As the image sensor 104, a time delay integration image sensor (TDIimage sensor) formed of multiple one-dimensional image sensorstwo-dimensionally arranged may be employed. In this case, a signaldetected by the respective one-dimensional image sensor is transferredto the next-stage one-dimensional image sensor in synchronism with themovement of the stage 12 and is added. Thereby detection can beperformed with relatively high speed and sensitivity. As the TDI imagesensor, a parallel-output sensor having multiple output taps may beused. Thereby, outputs from the sensor can be processed in parallel, sothat even higher speed detection can be performed.

In the case that the sensor for emanating UV light emanating is employedas the light source 101, a sensor of a reverse side irradiation type maybe used as the image sensor 104. In this case, the detection efficiencycan be improved to be higher than in the case that a sensor of anobverse surface irradiation type is used.

Numeral 14 denotes an image edit section configured to include apreprocessing section 106 and an image memory 107. The preprocessingsection 106 performs image corrections, such as shading correction anddark level correction, for digital signals detected by the detectionsection 13, and the image memory 107 stores corrected digital signals.

Numeral 15 denotes an image comparison processing section that performsan operation including calculation for determining a defect candidate inthe wafer or sample. The operation compares images of correspondingregions stored in the image memory 107 of the image edit section 14,performs a statistical process to extract an offset value, anddetermines it to be a defect. More specifically, the operation firstreads out digital signals of an image of an inspection target region(which hereinbelow will be referred to as a “detection image”) and animage of a corresponding region (which hereinbelow will be referred toas a “reference image”) stored in the image memory 107. Then, acorrection amount for positional alignment is calculated in a positionshift detecting section 108, and positional alignment of the detectionimages and the reference image is performed by using a correction amountcalculated by a statistical processing section 109. Then, using afeature amount of a corresponding pixel to a pixel having a statisticaloffset value is output to be a defect candidate. A parameter settingsection 110 sets an image processing parameter, such as a thresholdvalue to be used in extraction of a defect candidate from a differentialvalue, and supplies it to the statistical processing section 109. Then,in a defect classifying section 111, a true defect is extracted from afeature amount of the respective defect candidate and is classified.

Numeral 16 denotes a total control section that includes a CPU (built inthe total control section 16) and that is connected to a user interfacesection 112 (GUI (graphical user interface)) and a storage section 113.The user interface section 112 has display means and input means withwhich, for example, changes in inspection parameters (threshold valueand the like to be used in image comparison) are received from users anddetected defect information is displayed. The storage section 113stores, for example, feature amounts and images of defect candidates.Numeral 114 denotes a mechanical controller that drives the stage 12 inaccordance with a control instruction received from the total controlsection 16. The image comparison processing section 15, the detectionsection 13, and the like also are each driven in accordance with aninstruction received from the total control section 16.

As shown in FIG. 2, a large number of chips having the same pattern areregularly arranged in the semiconductor wafer 11. The total controlsection 16 performs control as follows. The semiconductor wafer 11 beingused as a sample is continually moved with the stage 12, and images ofthe chips are sequentially detected and retrieved by the detectingsection 13 in synchronism with the movement of the semiconductor wafer11. Then, for the detection images, in accordance with theabove-described procedure, digital image signals of regions 61, 62, 64,and 65 are compared as reference images to, for example, a region 63 ofthe detection image of FIG. 2, which is located in the same position asthe regularly arranged chips, whereby, a pixel having a statisticaloffset value is detected as a defect candidate.

The detection section 13 of the pattern inspection apparatus accordingto the present embodiment has multiple shiftable detection systems. FIG.3 shows a detailed view of the detection section 13.

In the configuration in FIG. 3, a case where, as the light source 101, apulse laser light source for emanating pulse laser of ultraviolet light(UV light, such as deep ultra violet light (DUV light)) is used.

Numeral 2310 denotes a quasicontinuous optical system. With this system,laser corresponding to one pulse of pulse laser emanated from the lightsource 101 is resolved into multiple pulses without changing the totalamount of light, Thereby, the peak value of the pulse laser emanatedfrom the light source 101 is reduced, and the number of pulses isincreased, whereby the light is rendered quasicontinuous. In thismanner, the time-wise amounts of light are averaged or substantiallyequalized. The light (laser) passed through the quasicontinuous opticalsystem 2310 is incident on a light-path splitting optical system 23. Thelight source 101 may be either a visible light laser or a lamp. In thiscase, however, the quasicontinuous optical system 2310 is not necessary.

The light incident on the light-path splitting optical system 23transmits through coherence reducing means 2302, and is then split by abeamsplitter 2301 for two light paths 2601 and 2602. The light split forthe light path 2601 is incident on a beam forming optical system 201,and is subjected to, for example, beam diameter regulation andilluminance regulation in the beam forming optical system 201. The lightemanated from the beam forming optical system 201 is bent by a mirror202, and is incident on a coherence reducing optical system 203. Then,temporal and spatial coherence is reduced in the coherence reducingoptical system 203. The light output from the coherence reducing opticalsystem 203 is incident on a deformation illumination optical system 20,and is regulated so that an illuminance distribution at the pupilposition of the objective lens 103 becomes a desired distribution. Ofthe light output from the deformation illumination optical system 20, anS polarized component is reflected in a polarization beam splitter 27 tothe side of the objective lens 103, and is led to irradiate the wafer 11through a light modulator unit 21 and the objective lens 103.“Brightfield illumination” hereinbelow refers to illumination of thewafer 11 by the light that has advanced along the above-described lightpath and has transmitted through the objective lens 103.

With the light having thus been split to the side of the light path2601, illumination adaptable for a wafer having undergone variousprocesses can be implemented. This can be implemented in the manner thatthe illuminance distribution of the illumination light at the pupilposition of the objective lens 103 is changed into multiple types ofillumination distribution by using the deformation illumination opticalsystem 20. The deformation illumination optical system 20 may be, forexample, a filter having the light transmittance changed on an opticalaxis cross section or an optical device that forms four or eight lightfluxes arranged point-symmetric about the optical axis. Stillalternatively, a device capable of oscillatorily moving beams may beused to change the beam position. The device capable of oscillatorymoving the beams is, for example, a galvano-mirror or a semiconductorresonance mirror. The deformation illumination optical system 20 isconfigured to switchably use such the devices.

The light split by the beamsplitter 2301 to the side of the light path2602 is transmitted through the coherence reducing optical system 203.Then, the light enters a polarization-light darkfield illuminationoptical system 24, and is further split by a partial mirror 2401 for twolight paths. One ray of the split light transmits through opticaldevices 2403 and 2405 and enters a polarization-light darkfieldillumination optical system 25. The other ray of the split light isreflected off of a total reflection mirror 2402, transmits through theoptical devices 2403 and 2405, and then enters the polarization-lightdarkfield illumination optical system 25. The respective rays of lighthaving entered the polarization-light darkfield illumination opticalsystem 25 transmit through optical devices 2501 and 2502, are reflectedoff of mirrors 2503 and 2504, thereby irradiating the surface of thewafer 11 from a diagonal direction. “Darkfield illumination” hereinbelowrefers to the illumination of the surface of the wafer 11 from thediagonal direction.

Of the respective rays of reflected light of the light traveled alongthe light path 2601 or 2602 and irradiated on the wafer 11, the ray ofthe light collected by the objective lens 103 transmits through thelight modulator unit 21, the polarization beam splitter 27, and a lightmodulator unit 22, and is then imaged on a detection face of the imagesensor 104. An optical image thus imaged is detected by the image sensor104. A detection signal of the image sensor 104 is converted by the A/Dconverter 105 into a digital signal, and the digital signal is outputfrom the detection section 13. Multiple detection signals are output inparallel from the image sensor 104. The multiple detection signal outputin parallel are A/D converted and output in parallel.

The light modulator unit 21 controls, for example, the light amounts andphases of illumination light derived from the light split to the side ofthe light path 2601 and reflected light from the wafer 11. For example,the light modulator unit 21 regulates a light amount ratio between raysof zero order and high order diffractive light reflected off the wafer11, thereby to improve the contrast of a circuit pattern signal detectedby the image sensor 104. Alternatively, the contrast of a circuitpattern is improved through polarization differential interference. Thelight amount ratio between rays of zero-order and high-order diffractivelight reflected from the wafer can be regulated in the manner that a ½wavelength plate and a ¼ wavelength plate are provided in the lightmodulator unit 21, whereby the rays are combined to change the lightoscillation direction. In addition, the polarization differentialinterference can be implemented in the manner that a birefringence prismis provided in the light modulator unit 21 and is used. A physicalphenomenon resulting from a polarization differential interferenceoptical system using a single Nomarski prism is similar to thatresulting from a general differential interference microscope. In thelight modulation unit 21, such a ½ wavelength plate, ¼ wavelength plate,and birefringence prism are provided to be shiftable by means (notshown).

The light modulator unit 22 is placed in a position conjugated with thepupil position of the objective lens 103, thereby enabling opticalmodulation at the pupil position. For example, a dielectric film isvapor-deposited in a central portion of a transparent substrate such asquarts, and the transmittance of the dielectric film portion is changed,thereby to modulate the light detected by the image sensor 104. In thiscase, a unit light-shielded with metal may be used instead of thedielectric film. The unit also is provided to be shiftable.

As described above, the configuration has, for example, the lightmodulator unit and the function of synchronously irradiating thedeformation illumination and the darkfield illumination with thebrightfield illumination, whereby optimal inspection can be performed byselection of an optimal optical system corresponding to, for example,the type of a defect desired to detect. However, for detecting even moretypes of defects, it is effective to perform the inspection of a singlewafer 11 under multiple optical conditions. In this case, the followingoperations are carried out in time series under the multiple opticalconditions: (1) selection of an optical condition; (2) acquirement of animage by the image sensor 104; (3) extraction of a defect candidate bythe image comparison processing section 15; and (4) repetition of defectdetection and classification. The results of these operations can bediscretely displayed in units of the optical condition, as shown in 1102of FIG. 4. Alternatively, as shown in 1102 of FIG. 4, a logical AND,logical OR, or the like of the results of the detections performed underthe respective optical conditions is obtained, thereby enabling it todisplay the synthesized results.

The detection results may be displayed as they are to show the presenceor absence of defects. Alternatively, however, as shown in 1101 and1102, the results of classification of detected defects may be displayedon a map, thereby to enable a user to know at a glance which conditionmakes it possible to optimally detect a target defect. In addition,presentation of a display on a single map in a different color in unitsof the condition enables the user to know at a glance correlationsbetween the respective conditions and detected defects.

In addition, in the configuration of the inspection apparatus of thepresent embodiment, not only that the results of detection undermultiple optical conditions are simply integrated and displayed, butalso the defect detection sensitivity can be improved by performing thefollowing operations. The operations are: (1) integration of imagescaptured under multiple optical conditions; (2) extraction of defectcandidates by the image comparison processing section 15 by using theimage integrated in (1); and (3) detection and classification ofdefects. Effects of image integration are shown in FIGS. 5A to 5E.Numeral 31 in FIG. 5A and numeral 32 in FIG. 5C indicate defect signalsof images acquired under respective optical conditions A and B differentfrom one another. Numeral 33 in FIG. 5B denotes a signal representing adifference when the image signal of FIG. 5A acquired under the opticalcondition A has been compared to an adjacent cell. An image containing adefect signal is used as an inspection image and as a reference image.Accordingly, the defect signal is inverted as a difference from thecomparison image and hence the signal appears in two portions, however,the defect signal 33, 34 is weak. In comparison, numeral 35 in FIG. 3Edenotes a signal representing a difference in the event that the imagesrespectively acquired under the optical conditions A and B aresynthesized, and the synthesized image then is compared to the adjacentcell. Thus, the images under the different optical conditions aresynthesized and processed, thereby enabling it to emphasize the defectand thence to implement high sensitivity inspection.

FIG. 6 shows an example of an image synthesis implementation method. Tobegin with, as described above, inspection is performed in the timeseries under a different optical condition (at step 801). Inspection isthen performed in the time series, and a defect candidate is extracted(at step 802). A reference image corresponding to an image of a localregion including the defect candidate is selected (which image hereafterwill be referred to as “defective image”), and the image is stored inthe storage device 113 (at step 803). Upon completion of extraction ofdefective images under all the conditions (at step 804), defectiveimages under respective conditions matching with one another in thecoordinates are extracted (at step 805). Then, the extracted defectiveimages are synthesized, and a synthetic defective image is therebycreated (at step 806). The created synthetic defective image againundergoes comparison with the reference image (at step 807). Thereby, adefect is detected (at step 808) and displayed (at step 809). In theabove process, at the stage of extracting the defect candidate (at step802), high sensitivity inspection including checking of misreportedmatter is performed, and a misreported matter is eliminated inre-inspection (at step 808) using the synthetic image, whereby highsensitivity inspection of small defects can be implemented.

According to a different mode of the image-synthesis inspection, imagesunder different optical conditions can be synchronously acquired. By wayof an example of the configuration capable of synchronously acquiringimages under two different optical conditions, FIG. 7 shows a simplifiedview of the configuration shown in FIG. 3.

In the configuration shown in FIG. 7, light emanated from the lightsource 101 travels through an illumination formation optical system 902and enters the polarization beam splitter 27. For a lamp being used as alight source, a lamp such as a mercury lamp or xenon lamp can be used.For a laser oscillator, the light source may be a type that performswavelength conversion of solid YAG laser (wavelength: 1024 nm) by usingnonlinear optical crystal or the like to thereby generate, for example,third and fourth harmonics of the fundamental wave. Alternatively, theusable light source may be an excimer laser or ion laser of a 248 nmwavelength or the like. Still alternatively, the light source may be,for example, an electron-beam gas emission lamp that outputs light atmultiple wavelengths (wavelengths are, for example, 351 nm, 248 nm, 193nm, 172 nm, 157 nm, 147 nm, 126 nm, and 121 nm). The illuminationoptical system 102 is of the type that regulates the characteristics ofillumination light for irradiating the wafer 11 and that is configuredto include a relay lens, an aperture diaphragm, and a wavelengthselection filter, for example.

The light reflected by the polarization beam splitter 27 is led toirradiate the wafer 11 through the objective lens 103. Reflected lightof the irradiation light from the wafer 11 is collected through theobjective lens 103. A portion of the collected light is reflected off ofa mirror 91 and is then incident on an optical system 92, and the restof the collected light transmits therethrough and is incident on anoptical system 93. In this case, the objective lens 103 is capable ofperforming shifting-irradiation between the brightfield illumination anddarkfield illumination or synchronous irradiation thereof.

The configuration of the present embodiment includes two sets of imagesensor sections. The optical systems 92 and 93, respectively, have thefunction of performing light modulation of incident light and collectingthe light into image sensor sections 104-1 and 104-2, and are configuredto include, for example, relay lenses and light modulation filters 94and 95. The light modulation filters 94 and 95 are optical filtersdifferent from each other. As examples, the light modulation filter 94permits only light about the optical axis center to transmit, and thelight modulation filter 95 permits only light spaced away from theoptical axis center. As alternative examples, the light modulationfilter 94 may be of a type that permits only light having a wavelengthranged from 400 nm to 500 nm to transmit, and the light modulationfilter 94 may be of a type that permits only light having a wavelengthof 400 nm or less. The former filter combination is used to separatelydetect zero order light components and high order diffractive lightcomponents of the reflected light from the wafer 11. The latter filtercombination is used to acquire per-wavelength images in the event ofillumination with multi-wavelength light. Although the example cases oftwo types of filters are described above, the configuration is notlimited to the two types. Any other combination may be employed inasmuchas optical components (such as wavelengths, spatial frequencies, andlight modulation directions) obtainable by the light modulation filters94 and 95 are different from one another. The image sensor section104-1, 104-2 has the function of performing photoelectrical conversionand A/D conversion of incident light, in which the converted digitalsignal is output to the image edit section 14.

Thus, images synchronously acquired by two image sensors are compared inthe time series, as described above; and selected images (defectiveimages) are then synthesized and compared again, whereby defects arefinally detected. Thereby, the image acquirement time is the same as inthe case of a single optical condition. In order to further increase theprocessing speeds, the configuration may be formed to include two imagecomparison processing sections, whereby enabling parallel extraction ofdefect candidates under different optical conditions.

Thus, according to all of the example methods described above, defectcandidates are extracted in units of the optical condition, defectiveimages are synthesized and compared again, and the defects are thenfinally detected. FIG. 8 shows another example of a method of performingthe defect detection process after image synthesis. In the presentexample, respective images acquired with the same timing by two imagesensor sections are corrected by the image edit section 14 andpreliminarily synthesized. The synthesized image is processed by thecomparison processing section 15; defects are detected and classified;and the classified result is transferred to the total control section 16and is then displayed.

FIG. 9 is still another example of a method of performing the defectdetection process after image synthesis. In the present example, inaddition to an image synthesized in an image synthesizer section 1001,also original images imaged under respective conditions are corrected bythe image edit section 14, and are then input into the image comparisonprocessing section 15. Then, feature amounts of defect candidatesrespectively extracted from the original images and a synthetic imageare integrated and classified; and the classified result is transferredto the total control section 16 and is displayed. Of course, only therespective original image can be input into the image comparisonprocessing section 15 without using the synthetic image.

As described above, according to the inspection apparatus described withreference to the respective embodiments of the present invention,defects are detected from images acquired under multiple opticalconditions, and the results are integrated. Alternatively, defects aredetected from the result of integration of images imaged underrespective conditions. Still alternatively, defects are detected throughthe integration of defect information detected from images imaged underrespective conditions. Thereby, high sensitivity detection of varioustypes of defects can be implemented.

While various types of defects of interest to users occur, there arecases where the respective types of defects are detectable bycombination of target dependant factors, such as the type, material,surface roughness, size, depth, pattern density, and pattern directionof and on a sample being used as an inspection target, andoptical-system dependant factors, such as illumination conditions.According to the present invention, such various types of defects can bedetected in a wide range in the manner that the multiple opticalconditions are selected, and information obtainable from acquired imagesare integrated.

The following will describe a process of the image comparison processingsection 15 for detecting images will now be described herebelow. FIGS.10A and 10B are schematic views showing one example of a semiconductorpattern as an inspection target. As shown in FIG. 10A, the semiconductorpattern has gates at a pitch of about 200 nm; and as shown in FIG. 10B,the variation in the linewidth thereof is about 20 nm. Numeral 51 ofFIG. 10C denotes a brightness waveform along a comparison direction oftarget patterns having such small variations, and the wavelengthindicates brightness value variations.

FIGS. 11A to 11D show an example of defect detection through the cellcomparison from an image having brightness variations. The cellcomparison compares images of adjacent cells with one another for thememory mat portion 601 formed of the group of small repetitious patterns(cells) shown in FIG. 2, and detects a portion where the brightnessdifference therebetween is greater than a threshold value is detected tobe a defect. Numeral 1301 in FIG. 11A denotes a defect signal. In thiscase, there are brightness variations, such that, as in the conventionalmanner, when a comparison is made to a pixel spaced away at a cellpitch, cases occur in which the difference therebetween is reduced(1301(C)) and, on the other hand, the difference between normal pixelsis increased (1301(D)). Alternatively, however, when a threshold valueis set so as not to detect the normal pixel to be a defect, the defectis overlooked or not detected. As such, in the present embodimentmethod, a reference pattern is created from corresponding multiplecells. First, at least one or a greater specific number of correspondingpixels of neighboring cells are collected for a target pixel f. FIG. 12shows an example case in which brightness values of corresponding pixelsof six neighboring pixels are collected. Then, an average brightnessvalue is calculated (in accordance with Equation 1), and the result isset as a reference image g. In Equation 1, f(i) is a brightness value ofa corresponding pixel of a neighboring cell, and N is the number ofcollection pixels.

$\begin{matrix}{g = {\sum\limits_{i = 1}^{N}{{f(i)}/N}}} & (1)\end{matrix}$

Numeral 1302 of FIG. 11B denotes a reference signal calculated from thedefect signal 1301 of FIG. 11A. Numeral 1303 of FIG. 11C denotes adifferential image representing the difference when compared to anadjacent cell; and numeral 1304 of FIG. 11D denotes a differential imagewith respect to a reference cell according to the present embodimentmethod. Thus, the stable defect detection can be implemented and theinfluence of bright nonuniformity resulting from the linewidthdifference and the like can be reduced through the comparison performedin accordance with the present embodiment method. Similar effects takeplace as well in the case of inter-chip comparison. According to theconventional chip comparison, in the case that, basically for theperipheral circuit portion (shown by 602 in FIG. 2B), in the case that,for example, the region 63 is an inspection target region in FIG. 2A,the image of the region 63 is compared with the image of the region 62or 64, a portion where the brightness difference between the images isgreater than the threshold value is detected to be a defect. However,according to the present embodiment method, a reference region isobtained through calculation from at least one or multiple correspondingregions, such as regions 61, 62, 64, and 65, for example, in accordancewith Equation 1.

The scope of the present invention includes even the following detectionmanner. With reference to the case shown in FIGS. 2A and 2B, as shown inEquation 1, a single reference pattern image is not created from, forexample, an average value or the like, but one-to-one comparisons areperformed in multiple regions between the regions 63 and 61, betweenregions 63 and 62, between . . . and . . . , and between the regions 63and 65, for example, and then, all the comparison results arestatistically processed to thereby perform defect detection.

FIGS. 13A and 13B show an example of the process that the imagecomparison processing section 15 executes by using multiple items ofdata as described above. Operation in the state of FIG. 11A is performedas follows. First, in synchronism with the movement of the stage, thedata are input in series into the memory 107, and stored partial imagesof multiple chips (61 to 65 of FIG. 2A) are sequentially read out. Then,calculations are carried out to obtain the amounts of position shifts ofcorresponding regions for performing the comparison using the multipleregions in the position shift detecting section 108. Then, as shown in aportion 1501, positional alignment of the respective regions is carriedout (arrangement and alignment). This is done for the reason that,because of, for example, vibration of the stage and tilt of a wafer seton the stage, corresponding chips do not generate signals at exactly thesame position in the acquired images. For the calculations of theamounts of position shifts, various methods are available, such asmethods using inter-image normalized correlations, sums of inter-imagegray scale differences, and sums of inter-image squares, and any of suchmethods may be used.

Subsequently, as shown in FIG. 13B, a comparator section 109 detects astatistical offset value to be defect. In this case, the inspectiontarget chip is assumed to be the chip 63. To begin with, as describedabove, the images 61, 62, 64, and 65 are integrated (at step 1502), anda reference image is created thereby. In this case, while the referenceimage can be compared with the image 63, noise from the image 63 can beremoved. A method for the noise removal may be of the type simply usinga Gaussian filter or a smoothing filter performing the moving averagingoperation; or alternatively, may be of the type removing noise ofspecific frequencies by, for example, FET or wavelet processing (at step1503). Then, an N feature amounts (N: integer greater than or equalto 1) are obtained from the integrated reference image, which has beencreated through synthesis, and the respective pixels of the image(detection image) of the inspection target chip, from which noise hasbeen removed (at step 1504), thereby to form an N-order feature spacing(at step 1507).

Examples of the feature amounts include, but not limited to, brightnessvalues of the respective detection image and reference image and anaverage brightness value of both images, contrast, standard deviationacross neighboring pixels, brightness differential between the detectionimage and the reference image, increase and decrease with respect theneighboring pixel, quadratic differential value. Any other factors maybe used inasmuch as they represent features of respective images. Inaddition, of the N feature spacings, spacings may be formed by selectingfeature amounts effective for defect determination. Then, clustering iscarried out over the feature spacings, and pixels falling in thestatistically offset value are extracted to be defect candidates (atstep 1508).

According to the present invention, without performing the synthesis ofreference images from images of multiple chips, it is possible tocompare respective reference image with an inspection target chip. Morespecifically, with respect to the inspection target chip 63, a featureamount calculation is performed from the chip 63 and respective pixelsof the chip 61 (at step 1505). Similarly, calculations are sequentiallyperformed to obtain feature amounts from the chips 63 and 62, the chips63 and 65, and the chips 63 and 65, and the feature spacing is formedfrom the respective results of the calculations. Then, an offset valueis determined to be a defect. Alternatively, the inter-chip comparisonsare not performed, but the feature amounts are calculated using therespective images of the chips 61 to 65 (at step 1506) to form thefeature spacing, whereby making it possible to determine a offset valueto be a defect.

The threshold value for detecting offset values (for example, a distancefrom a normal pixel distribution for determining offset values in thefeature spacing) is determined statistical data such as an average valueor standard deviation in the brightness for forming the spacing).

The method, which uses the multiple items of data to detect a pixelfalling within the statistical offset value, is adaptable as well to thecell comparison. Generally, in the case that a peripheral circuitportion and memory mat portions are mixed in a inspection target chip asshown in FIG. 2B, the chip comparison (dye comparison) is applied forthe peripheral circuit portion, and the cell comparison is applied forthe memory mat portions. In the present embodiment inspection, theimages of the peripheral circuit portion and the memory mat portions areacquired with the same timing, the image detection with the stagemovement is performed at one time, and the chip comparison and the cellcomparison are performed in parallel. These can be accomplished in themanner that the image comparison processing section 15 is configured toinclude a chip-comparison dedicated image comparison processing circuitsection and a cell-comparison dedicated image comparison processingcircuit section, in which the chip comparison and the cell comparisonare performed in the respective dedicated image comparison processingcircuit sections. Consequently, the process times are the same in thecases that the inspection of the overall inspection target chips isperformed by the chip comparison and the combination of the chipcomparison and the cell comparison. An area for the cell comparison andan area for the chip comparison can either be manually input by a userwhile seeing an image of chips or be automatically specified with CAD(computer-aided design) data being input. The process may be arranged toallow the execution of parallel processing of cell comparisons andparallel processing of chip comparisons, in which the even higher speedimage process can be implemented by combination of the parallelprocessings of the chip comparisons and the cell comparisons.

According to such a simple method, however, while the statisticallyoffset values within the partial image are thus extracted to be defectcandidates, a non-defect (misreported matter) may probably be includedin the inspection result. FIG. 14A is a reference image, and FIG. 14B isa detection image. In these cases, a significant brightness variation iscaused in a background portion 1801. In addition, in a pattern 1802, asignificant brightness variation is caused, and light/dark inversion iscaused with respect to similar neighboring patterns. In an image of FIG.14C, 14D, while pattern inversion is not caused, a defect 1803 ispresent. In such local images, a light/dark inverted pattern in FIG. 14Band a defect 1803 in FIG. 14D, respectively, are detected to be offsetvalues. In these cases, there is a high probability that many invertedpatterns similar to that described above have occurred in a neighboringregion of the image of FIG. 14B. As such, according to the presentembodiment method, offset values in such a local region are collectedfrom a wider region, items of information of the offset values areintegrated, and a final offset value is detected to be a defect.

FIG. 15 shows an example of the process described above. To begin with,feature amounts are extracted from defect candidates extracted as offsetvalues within a local region in one of the manners of the chipcomparison (dye comparison), cell comparison, and the comparison byparallel combination of the chip comparison (dye comparison) and thecell comparison (at step 1601). In this step, in addition to per-pixelfeature amounts, feature amounts of defects in, for example, the areasand shapes of the respective defects and background texture arecalculated. These defect candidates are each extracted from a localregion in a chip such as the portion 63 of FIG. 6.

Subsequently, defect candidates are collected from a wider region, suchas one line of chips as shown in a portion 1701 of FIG. 11B, or from allchips in the wafer (at step 1602). Not only per-pixel feature amounts,but also factors such as feature amounts of defects, intra-wafercoordinates, and intra-chip coordinates are used as feature amounts,thereby to again form a feature spacing (at step 1603).

Then, at least one or multiple optimal feature amounts are selectedcorresponding to, for example, the process and type of the target wafer,and linear transformation or the like of the spacing is performed,whereby the feature spacing is optimized to facilitate theidentification between defects and the misreported matters (at step1604). Further, the occurrence density, repeatability, and the likewithin the chip are taken into account, and statistically offset valuesare detected (at step 1605). The offset values are recognized to befinal defects, and are classified corresponding to the types of defects(at step 1606). Thereby, defects and misreported matters, of whichidentification is difficult, are identified with high accuracy.

In an example of an inspection apparatus using the present embodimentmethod, past inspection results are stored in the storage device 113,and defects and misreported matters can be identified with high accuracyby using the stored inspection results (portion 1702 of FIG. 11A). Forexample, when it is preliminarily known whether past inspection resultsare critical or non-critical defect in the subsequent inspection, theresults are stored with defective images, feature amounts, andcriticality/non-criticality information. The information as well isreflected into, for example, a threshold value and a determination rulefor final defect detection and classification. Alternatively, the defectand misreported matter can be identified from one another by comparisonof the feature amount of the detected defect with a feature amount of adefect detected in past inspection, of which thecriticality/non-criticality is preliminarily known.

Further, in the present embodiment method, in the case that a patternprone to a defect is preliminarily known, the pattern can be detectedwith high sensitivity. With reference to FIG. 17A, numeral 63 denotes adetection image of a part of a inspection target chip, numerals 62 and64 denote reference images of portions corresponding to the left andright chips. Essentially, numerals 41 to 43 of the chip 63 each denotefour hole patterns arranged in the 2×2 form, which are supposed to beidentical to one another. In the case of the hole pattern 41, of thefour hole patterns, two left hole patterns are different in brightnesson the image from the hole patterns 42 and 43 due to insufficientfilling. In comparison, in the image 62 adjacent to the left side, theoverall hole pattern is dark in comparison to the hole pattern of theimage 63 due to brightness variations in association with the filmthickness difference. For this reason, compared to the images 62 and 63,differences across all the hole patterns are increased, so that also thehole patterns 42 and 43 are detected to be defects. On the other hand,in the case of the image 64 adjacent to the right side, ambientbrightness variations are caused on the base, so that compared to theimages 63 and 64, the difference of the base is larger than thedifference of the defect, therefore permitting overlooking of thedefect. Thus, in the event that brightness variations are caused in thepart between the comparison images, the influence thereof makes itdifficult to detect only a real defect with high sensitivity.

In the comparison inspection according to the present invention, in theevent that a pattern like the pattern 41 prone to a critical defectwhich is desired to be precisely inspected is preliminarily known, evenwhen brightness variations are caused in various portions, the specificpattern can be inspected with high sensitivity without being influencedby the variations.

FIG. 17C shows a flow and a procedure of the aforementioned inspection.To begin with, a pattern specifically desired to be inspected with highsensitivity is specified by a user from detection images (at step 401).Numeral 44 of FIG. 17B is an example specified in a rectangular form.According to the present invention, a pattern thus specified is set tobe a template and stored (at step 402). In this case, multiple templatesmay be set. When the inspection image is input, patterns similar to thetemplate within a detection image (such as the image 63) is searched forby the image comparison processing section 15 (at step 403). Further,patterns similar to the template is searched for within images (such asthe images 62, 64, . . . ) in corresponding positions of multipleperipheral chips (at step 404). Then, feature amounts of patternssearched for are calculated (at step 405). The feature amounts may beany factor that represents a feature of the multiple patterns, such asvariances or contrast average val. Then, a statistical offset value isdetected to be a defect (at step 406).

Thus, the offset value is detected from the feature amounts only of thesame multiple patterns, whereby even when significant brightnessvariations occur in peripheral portions thereof, a defect desired by theuser can be detected with high sensitivity without having the influenceof the variations.

Further, in the comparison inspection according to the presentinvention, an item (“nuisance,” hereafter) that would be detected as adefect but would not be critical in the defect condition can be excludedfrom inspection targets.

FIG. 18 shows a flow of the aforementioned exclusion process, in which aprocedure similar to that of FIG. 17 is carried out. To begin with, apattern preliminarily known to be prone to a nuisance or nuisance defectis specified by a user from detection images (at step 701). According tothe present invention, a pattern thus specified is set to be a templateand stored (at step 702). In this case, multiple templates may be set.In the image comparison processing section 15, of an input image, anextracted pattern region is excluded (at step 704), and the defectdetection process is performed (at step 705). Further, a reference imagecorresponding to a detected defect is compared with the template (atstep 706). If they are the same pattern, then the defect is determinedto be a nuisance defect and hence excluded (at step 707).

Thus, the nuisance is preliminarily excluded from the inspection targetregion, whereby the influence on detection of a critical defect isreduced so as to not detect such a nuisance defect.

FIGS. 19A to 19C show an example of performing high accuracy, high speedsearching for a pattern similar to a set template. A case is now assumedsuch that it is preliminarily known that, as shown in FIG. 19A,repetitious patterns similar to a target pattern to be specified arepresent. In this case, after the target pattern is specified by a user(at step 2001), a repletion pitch of the patterns is input by the useras layout information (at step 2002). Thereby, the search region isrestricted. Then, search chips are specified from a chip arrangement ofan inspection target wafer, as shown in FIG. 19B (at step 2003). Inaddition, the search range in the image is specified, as shown in FIG.19B (at step 2004). In searching, patterns similar to the template inthe detection are first searched for; and when a target pattern has beendetected, then searching is performed for a neighboring portion spacedaway by the pattern pitch in a range set from the coordinates of thetarget pattern (at step 2005). Then, in an image of a set referencechip, a neighboring portion at the same coordinates as those of thepreviously extracted is searched, and patterns are extracted (at step2006). In the case of CAD information, the layout information needs notto be input by the user, but the information can be input from the CADinformation. In addition, when the pattern position specified from thepast inspection information, pattern search can be performed byrestricting the search range to peripheral portions of that position.

According to the present invention, the above-described high sensitivityinspection of a specified pattern can be performed concurrently with aninspection with a specified pattern being masked.

The above is the flow of the defect detection and classification processaccording to the present invention, interim results, detection results,and the like are graphically displayed on the GUI 112 (user interfacesection). Examples of the displays are shown in FIG. 20. In many casesof conventional inspection, the results thereof are displayed in theform of a wafer map as shown in the portion 1701 of FIG. 16B. However,in the present embodiment method, an N-order feature spacing as shown ina portion 1901 of FIG. 20 is additionally displayed. When a pointplotted in the spacing is specified, an image, a feature amount, and thelike corresponding to the specified portion are displayed. Afterverification of these items of information, when an offset value isfound to be a misreported value, a user is permitted to carry out, forexample, adjustment or reselection for the feature amount. According tothe present invention, as shown in a portion 1902, an offset value isagain detected from stored feature amounts in response to the selectionthereof, so that the user is able to instantly verify variations in thederivation thereof. Further, while verifying the offset value in thefeature spacing, the user can concurrently tune the threshold value forfinal detection, as shown in a portion 1903. As described above,basically, in feature amount selection, the default value is initiallyused, and the result of defect and misreported matter detection ischecked and concurrently tuned by the user. Alternatively, however, theselection may be carried in the manner that the user preliminarilypresents teaching regarding the results of determination as to whethersome defect candidates are defects or misreported matters, therebyenabling automatic selection of a feature amount so as to optimize thedegree of separation of the defect candidates presented with theteaching to be highest.

The above-described process of the image comparison processing section15 is accomplished through software processing by the CPU. However,arithmetic portions serving as cores for, for example, operation ofnormalized correlations and forming of feature spacings, may be includedin hardware processing by an LSI or the like. Thereby, improvement ofthe processing speeds can be achieved. Further, the present inventionenables the detection of defects in the range of from 20 nm to 90 nmeven when there occur a delicate difference caused in the pattern filmthickness after planarization processing such as CMP, and/or asignificant brightness difference between dyes in association with ashortened wavelength of illumination light.

Further, suppose that the inspection is performed for low-k films, suchas inorganic insulation films such as SiO₂, SiOF, BSG, SiOB, and porousciliary films, and organic films such as methyl-group containing SiO₂,MSQ, polyimide based films, a parylene based film, a Teflon™ base film,and an amorphous carbon film. In this case, according to the presentinvention, even when local brightness variations are present inassociation with intra-film variations in refraction index distribution,defects in the range of from 20 nm to 90 nm become detectable.

The one embodiment of the present invention has thus been described withreference to the exemplary case of the defect inspection method in theoptical exterior inspection device for the semiconductor wafer used asthe target. However, the present invention can be adapted for comparisonimages in electron beam pattern inspection. The inspection target is notlimited to the semiconductor wafer, but the present invention isadaptable for other types of inspection targets, such as TFT substrates,photomasks, printed circuit boards.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentembodiment is therefore to be considered in all respects as illustrativeand not restrictive, the scope of the invention being indicated by theappended claims rather than by the foregoing description and all changeswhich come within the meaning and range of equivalency of the claims aretherefore intended to be embraced therein.

FIG. 2

-   Chip-   Peripheral Circuit Portion-   Memory Hat Portion    FIG. 13A-   Positional Offset Detection Arrangement And Alignment-   Positional Offset Amount    FIG. 13B-   1502 Integration Of Multiple Chips 61, 62, 64, And 65-   1503 Elimination Of Noise Of Inspection Target Chip 63-   1504 Feature Amount Calculation With Inspection Target Chip-   63 And Integration Reference Chip-   1505    -   Feature Amount Calculation With Chips 63 And 61    -   Feature Amount Calculation With Chips 63 And 62    -   Feature Amount Calculation With Chips 63 And 65-   1506    -   Feature Amount Calculation With Chip 61    -   Feature Amount Calculation With Chip 62    -   Feature Amount Calculation With Chip 65-   1507 N-Dimensional Feature Spacing-   1508 Statistical Offset Value Detection By Clustering (Defect    Candidate Extraction)    Defect Candidate    FIG. 17A-   Chip 62-   Chip 63-   Chip 64    FIG. 17B-   Template 1-   Template 2    FIG. 17C-   401 Target Pattern Specification By User-   402 Set Template-   403 Search For Similar Patterns In Detection Image-   404 Search For Similar Patterns In Reference Images (Multiple)-   405 Calculate Feature Amounts Of Extracted Patterns-   406 Detect Statistical Offset Values-   701 Target Pattern Specification By User-   702 Set Template-   703 Search For Similar Patterns In Detection Image-   704 Exclude Extracted Patterns From Image-   705 Detect Defect (Offset Value Detection)-   706 Compare Template And Reference Patterns Corresponding To    Defective Portion-   707 EXCLUDE Defect Having Sake Pattern As Template    FIG. 19-   2001 Target Pattern Specification By User-   2002 Input Layout Information Such As Pattern Pitches-   2003 Specify Search Chip-   2004 Specify Pattern Search Range (Defect Inspection Process Range)    Start Search-   2005 Search For Same Patterns In Detection Image-   2006 Extract Same Patterns As Extracted Pattern From Same Position    Of Reference Image-   Pattern Pitch 1-   Pattern Search Range-   Pattern Pitch 2-   Pattern Pitch 3-   Pattern Pitch 4-   Search

What is claimed is:
 1. A method of inspecting a defect, comprising:illuminating a pattern of a sample; acquiring a first image of thesample and a second image of the sample, based on light reflected fromthe pattern; and processing the first image and the second image,wherein the processing includes: setting a search range of the firstimage, based on information of a plurality of patterns and correspondinginformation of a plurality of pitches, wherein each pattern has aplurality of pitches, setting a search range of the second image, basedon the search range of the first image, searching the search range ofthe first image, and the search range of the second image, andextracting a defect candidate.
 2. A method of inspecting a defect,according to claim 1, wherein the search range of the second imagecorresponds to the search range of the first image.
 3. A method ofinspecting a defect, according to claim 1, wherein the search range ofthe first image includes patterns identical to or nearly identical tothe patterns in the search range of the second image.
 4. A method ofinspecting a defect, according to claim 1, wherein a die of a pattern inthe search range of the first image, and a die of a pattern in thesearch range of the second image are separated by more than two dies. 5.A defect inspecting apparatus for inspecting a defect, comprising: anilluminating portion configured to illuminate a pattern of the sample;an acquiring portion configured to acquire a first image and a secondimage from the sample, based on light reflected from the pattern; and aprocessing portion configured to process the first image and the secondimage, wherein the processing includes: setting a search range of thefirst image, based on information of a plurality of patterns andcorresponding information of a plurality of pitches, wherein eachpattern has a plurality of pitches, setting a search range of the secondimage, based on the search range of the first image, searching thesearch range of the first image, and the search range of the secondimage, and extracting a defect candidate.
 6. A defect inspectingapparatus for inspecting a defect, according to claim 5, wherein thesearch range of the second image corresponds to the search range of thefirst image.
 7. A defect inspecting apparatus for inspecting a defect,according to claim 5, wherein the search range of the first imageincludes patterns identical to or nearly identical to the patterns inthe search range of the second image.
 8. A defect inspecting apparatusfor inspecting a defect, according to claim 5, wherein a die of apattern in the search range of the first image, and a die of a patternin the search range of the second image are separated by more than twodies.
 9. A defect inspecting apparatus for inspecting defects ofpatterns of a sample, the apparatus comprising: a light sourceconfigured to illuminate the patterns of the sample; an image sensorconfigured to acquire a first image of the sample and a second image ofthe sample, based on light reflected from the patterns; and a defectextraction unit configured to extract defects by processing theinspection images; wherein the defect extraction unit is configured tospecify a pattern preliminarily known to be prone to a nuisance or acritical defect, to set a repletion pitch of patterns as layoutinformation, to set search chips from a chip arrangement of a surface ofthe sample, and to set a search range in the inspection images; andwherein the processing performed by the defect extraction unit includes:setting a search range of the first image, based on information of aplurality of patterns and corresponding information of a plurality ofpitches, wherein each pattern has a plurality of pitches, setting asearch range of the second image, based on the search range of the firstimage, searching the search range of the first image, and the searchrange of the second image, and extracting a defect candidate.
 10. Adefect inspecting apparatus for inspecting defects of patterns of asample, according to claim 9, wherein the search range of the secondimage corresponds to the search range of the first image.
 11. A defectinspecting apparatus for inspecting defects of patterns of a sample,according to claim 9, wherein the search range of the first imageincludes patterns identical to or nearly identical to the patterns inthe search range of the second image.
 12. A defect inspecting apparatusfor inspecting defects of patterns of a sample, according to claim 9,wherein a die of a pattern in the search range of the first image, and adie of a pattern in the search range of the second image are separatedby more than two dies.